Skip to main content

GEB simulates the environment, the individual behaviour of people, households and organizations - including their interactions - at small and large scale.

Project description

Installation

GEB can be installed with pip, including all dependencies on Windows, Linux and Mac OS X.

pip install geb

Overview

GEB (Geographical Environmental and Behavioural model) simulates the environment (e.g., hydrology, floods), the individual people, households and orginizations as well as their interactions at both small and large scale. The model does so through a "deep" coupling of an agent-based model a hydrological model, a vegetation model and a hydrodynamic model. You can find full documentation here.

The figure below shows a schematic overview of the model agent-based and hydrological model.

Schematic model overview of GEB.

Cite as

Model framework

de Bruijn, J. A., Smilovic, M., Burek, P., Guillaumot, L., Wada, Y., and Aerts, J. C. J. H.: GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model, Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, 2023.

Applications

Kalthof, M. W. M. L., de Bruijn, J., de Moel, H., Kreibich, H., and Aerts, J. C. J. H.: Adaptive Behavior of Over a Million Individual Farmers Under Consecutive Droughts: A Large-Scale Agent-Based Modeling Analysis in the Bhima Basin, India, EGUsphere preprint, https://doi.org/10.5194/egusphere-2024-1588, 2024.

Building on the shoulders of giants

GEB builds on, couples and extends several models, depicted in the figure below.

Model components of GEB.

  1. Burek, Peter, et al. "Development of the Community Water Model (CWatM v1.04) A high-resolution hydrological model for global and regional assessment of integrated water resources management." (2019).
  2. Langevin, Christian D., et al. Documentation for the MODFLOW 6 groundwater flow model. No. 6-A55. US Geological Survey, 2017.
  3. Tierolf, Lars, et al. "A coupled agent-based model for France for simulating adaptation and migration decisions under future coastal flood risk." Scientific Reports 13.1 (2023): 4176.
  4. Streefkerk, Ileen N., et al. "A coupled agent-based model to analyse human-drought feedbacks for agropastoralists in dryland regions." Frontiers in Water 4 (2023): 1037971.
  5. Joshi, Jaideep, et al. "Plant-FATE-Predicting the adaptive responses of biodiverse plant communities using functional-trait evolution." EGU General Assembly Conference Abstracts. 2022.
  6. Leijnse, Tim, et al. "Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind-and wave-driven processes." Coastal Engineering 163 (2021): 103796.

Developers (ordered by full-time equivalent working time on model)

Current or past contributors (in order of first to last contribution)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

geb-1.0.0b4.tar.gz (228.6 kB view details)

Uploaded Source

Built Distribution

geb-1.0.0b4-py3-none-any.whl (255.9 kB view details)

Uploaded Python 3

File details

Details for the file geb-1.0.0b4.tar.gz.

File metadata

  • Download URL: geb-1.0.0b4.tar.gz
  • Upload date:
  • Size: 228.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.4.22

File hashes

Hashes for geb-1.0.0b4.tar.gz
Algorithm Hash digest
SHA256 92d658105ff2dac4f030b5c2c4362941de690a71d8217cbc47fe4364f524cdbf
MD5 4b26960953132ec676009d12e2567060
BLAKE2b-256 a1c79f8905c3af1a224f4b184fa7ded98877f7c8e1718d14992a7110a93ed7ab

See more details on using hashes here.

File details

Details for the file geb-1.0.0b4-py3-none-any.whl.

File metadata

  • Download URL: geb-1.0.0b4-py3-none-any.whl
  • Upload date:
  • Size: 255.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.4.22

File hashes

Hashes for geb-1.0.0b4-py3-none-any.whl
Algorithm Hash digest
SHA256 e63d2bfb83c75692956d3d1a4a2d613feff8700045144e25db8959034833f04c
MD5 9e25f12e83b6fa7caae812bf11388a04
BLAKE2b-256 13dbb935859ba722e912cff02e55c031886c54b72f9ff26f52cd86c1e3027765

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page